NMRG LM. Contreras
Internet-Draft Telefonica
Intended status: Informational October 24, 2021
Expires: April 27, 2022
Evolution of Cooperating Layered SDN Architecture including Compute and
Data Awareness
draft-contreras-nmrg-clas-evolution-00
Abstract
This document proposes the extension of the Cooperating Layered
Architecture for Software-Defined Networking (SDN) framework by
including Compute and Data Awareness.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Cooperating Layered Architecture for Software-Defined
Networking (CLAS) . . . . . . . . . . . . . . . . . . . . . . 2
3. Augmentation of CLAS with Compute and Data Awareness . . . . 5
3.1. Compute Stratum . . . . . . . . . . . . . . . . . . . . . 5
3.2. Learning Plane . . . . . . . . . . . . . . . . . . . . . 5
3.3. Extended CLAS architecture . . . . . . . . . . . . . . . 6
4. TODO for next versions of this document . . . . . . . . . . . 7
5. Security Considerations . . . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 7
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 7
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 8
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 8
1. Introduction
Current telecommunication networks are evolving towards a tight
integration of interconnected compute environments, offering
capabilities for the instantiation of virtualized network functions
interworking with physical variants of other network functions,
altogether constituting services end-to-end.
Moreover, network operations are complementing the capabilities of
automation and programmability with the introduction of Artificial
Intelligence (AI) and Machine Learning (ML) techniques to facilitate
informed decisions as well as predictive behavior enabling consistent
closed loop automation.
It is then necessary to provide a network management framework that
could incorporate these trends in a smooth manner, structuring the
different concerns and the interaction among components operating the
network.
This document describes the evolution of one of those frameworks,
named Cooperating Layered Architecture for Software-Defined
Networking (CLAS) [RFC8597] to include the aforementioned aspects
into the architecture.
2. Cooperating Layered Architecture for Software-Defined Networking
(CLAS)
[RFC8597] describes an SDN architecture structured in two different
strata, namely Service Stratum and Transport Stratum. On one hand,
the Service Stratum contains the functions related to the provision
of services and the capabilities offered to external applications.
On the other hand, the Transport Stratum comprises the functions
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focused on the transfer of data between the communication endpoints
(e.g., between end-user devices, between two service gateways, etc.).
Each of the strata is structured in different planes, as follows:
o The Control plane, which centralizes the control functions of each
stratum and directly controls the corresponding resources.
o The Management plane, logically centralizing the management
functions for each stratum, including the management of the
control and resource planes.
o The Resource plane, that comprises the resources for either the
transport or the service functions.
Figure 1 illustrates original CLAS architecture.
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Applications
/\
||
||
+-------------------------------------||-------------+
| Service Stratum || |
| \/ |
| ........................... |
| . SDN Intelligence . |
| . . |
| +--------------+ . +--------------+ . |
| | Resource Pl. | . | Mgmt. Pl. | . |
| | |<===>. +--------------+ | . |
| | | . | Control Pl. | | . |
| +--------------+ . | |-----+ . |
| . | | . |
| . +--------------+ . |
| ........................... |
| /\ |
| || |
+-------------------------------------||-------------+
|| Standard
-- || -- API
||
+-------------------------------------||-------------+
| Transport Stratum || |
| \/ |
| ........................... |
| . SDN Intelligence . |
| . . |
| +--------------+ . +--------------+ . |
| | Resource Pl. | . | Mgmt. Pl. | . |
| | |<===>. +--------------+ | . |
| | | . | Control Pl. | | . |
| +--------------+ . | |-----+ . |
| . | | . |
| . +--------------+ . |
| ........................... |
| |
| |
+----------------------------------------------------+
Figure 1: Cooperating Layered Architecture for SDN
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3. Augmentation of CLAS with Compute and Data Awareness
The CLAS architecture was initially conceived from the perspective of
exploiting the advantages of network programmability in operational
networks.
The evolution of current telecommunication services and networks are
however introducing new aspects to consider.
o Consideration of distributed computing capabilities attached to
different points in the network, intended for hosting a variety of
services and applications usually in a virtualized manner (e.g.,
[I-D.contreras-alto-service-edge]).
o Introduction of Artificial Intelligence (AI) and Machine Learning
(ML) techniques in order to improve operations by means of closed
loop automation (e.g., [I-D.pedro-nmrg-ai-framework]).
With that in mind, this memo proposes augmentation to the original
CLAS proposition by adding the aforementioned aspects.
3.1. Compute Stratum
For the first aspect, the CLAS architecture is extended by adding a
new stratum, named Compute Stratum. The objective is to contain in
this stratum the control, management and resource planes related to
the computing part. As in the other two strata, the mission is to
make this additional stratum cooperate with the other two in order to
facilitate the overall service provision in the network.
With this addition, and in order to be more explicit in the strata
scope, the previously named Transport Stratum is renamed as Network
Stratum, representing the fact that this stratum responsibility is
focused on the overall connectivity supporting the other two strata
in the architecture.
3.2. Learning Plane
A further extension to the original CLAS architecture is related to
the need of collecting, processing and sharing relevant data from
each of the considered strata. With that purpose a Learning Plane is
proposed to complement the already existing planes per stratum.
The learning plane will be in charge of handling the data
specificities of each particular stratum. Thus, the learning plane
in the Service Stratum is focused on data relevant to the service as
defined by the application or service owner, usually in terms of
service key performance indicators (KPI) [TMV]. Then, the learning
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plane in the compute stratum concentrates on data related to the
computing capabilities in use (e.g., CPU load, RAM usage, storage
utilization, etc) [OpenStack]. Finally, the learning plane in the
network stratum is in charge of handling the monitoring and telemetry
information obtained from the network (e.g.,
[I-D.ietf-opsawg-service-assurance-yang]).
3.3. Extended CLAS architecture
Figure 2 presents the augmentation proposed showing the relationship
among strata.
Applications
/\
||
+-------------------------------------||-------------+
| Service Stratum || |
| \/ |
| +--------------+ ........................... |
| | Learning Pl. | . SDN Intelligence . |
| | |<===>. . |
| +-----/\-------+ . +--------------+ . |
| || . | Mgmt. Pl. | . |
| || . +--------------+ | . |
| +-----\/-------+ . | Control Pl. |-----+ . |
| | Resource Pl. | . | | . |
| | |<===>. +--------------+ . |
| +--------------+ ........................... |
| /\ /\ |
| || || |
+--------------------------------||-------------||---+
Standard API -- || -- ||
+--------------------------------||-----+ ||
| Compute Stratum || | ||
| \/ | ||
| +----------+ ................... | ||
| | Learning | . SDN . | Std. ||
| | Plane |<==>. Intelligence . | API ||
| +-----/\---+ . +----------+ . | -- || --
| || . | Mgmt. Pl.| . | ||
| || . +----------+ | . | ||
| +-----\/---+ . | Control |-+ . | ||
| | Resource | . | Plane | . | ||
| | Plane |<==>. +----------+ . | ||
| +----------+ ................... | ||
+----------------------------------/\---+ ||
Standard API -- || -- ||
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+-------------------||-----------||-----+
| Network Stratum || || |
| \/ \/ |
| +----------+ ................... |
| | Learning | . SDN . |
| | Plane |<==>. Intelligence . |
| +-----/\---+ . +----------+ . |
| || . | Mgmt. Pl.| . |
| || . +----------+ | . |
| +-----\/---+ . | Control |-+ . |
| | Resource | . | Plane | . |
| | Plane |<==>. +----------+ . |
| +----------+ ................... |
+---------------------------------------+
Figure 2: Extended CLAS architecture
4. TODO for next versions of this document
This version is a work-in-progress. Next versions of the document
will address somo further aspects such as:
o Communication between strata (and planes).
o Deployment scenarios (including legacy ones).
o Potential use cases (specially in alignment with on-going
activities in NMRG).
5. Security Considerations
Same security considerations as reflected in [RFC8597] with regards
to the strata architecture apply also here.
Apart from that, the introduction of the Learning plane on the data
management imposes additional security concerns. (TODO: elaborate on
data-related security issues).
6. IANA Considerations
This draft does not include any IANA considerations
7. References
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[I-D.contreras-alto-service-edge]
Contreras, L. M., Lachos, D. A., Rothenberg, C. E., and S.
Randriamasy, "Use of ALTO for Determining Service Edge",
draft-contreras-alto-service-edge-03 (work in progress),
July 2021.
[I-D.ietf-opsawg-service-assurance-yang]
Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
Arumugam, "YANG Modules for Service Assurance", draft-
ietf-opsawg-service-assurance-yang-01 (work in progress),
July 2021.
[I-D.pedro-nmrg-ai-framework]
Martinez-Julia, P., Homma, S., and D. R. Lopez,
"Artificial Intelligence Framework for Network
Management", draft-pedro-nmrg-ai-framework-00 (work in
progress), October 2021.
[OpenStack]
"OpenStack performance documentation",
https://docs.openstack.org/developer/performance-docs/
index.html .
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[RFC8597] Contreras, LM., Bernardos, CJ., Lopez, D., Boucadair, M.,
and P. Iovanna, "Cooperating Layered Architecture for
Software-Defined Networking (CLAS)", RFC 8597,
DOI 10.17487/RFC8597, May 2019,
<https://www.rfc-editor.org/info/rfc8597>.
[TMV] "Service performance measurement methods over 5G
experimental networks", 5G-PPP TMV white paper , May 2021.
Acknowledgments
The work of L.M. Contreras has been partly funded by the European
Commission through the H2020 project 5GROWTH (Grant Agreement no.
856709).
Author's Address
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Luis M. Contreras
Telefonica
Ronda de la Comunicacion, s/n
Sur-3 building, 3rd floor
Madrid 28050
Spain
Email: luismiguel.contrerasmurillo@telefonica.com
URI: http://lmcontreras.com/
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